Liquidity cost of market orders in the Taiwan Stock Market: A study based on an order-driven agent-based artificial stock market

An information-based multiasset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented and studied so as to determine the influences of agents’ networks on the market’s structure. Agents are organized in networks that are responsible for the formation of the sentiments of the agents. In the market, agents trade risky assets in exchange for cash and share their sentiments by means of interactions that are determined by sparsely connected graphs. A central market maker (clearing house mechanism) determines the price process for each stock at the intersection of the demand and the supply curves. A set of market’s structure indicators based on the main single-assets and multiassets stylized facts have been defined, in order to study the effects of the agents’ networks. Results point out an intrinsic structural resilience of the stock market. In fact, the network is necessary in order to archive the ability to reproduce the main stylized facts, but also the market has some characteristics that are independent from the network and depend on the finiteness of traders’ wealth.

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MARKET FLUCTUATIONS EXPLAINED BY DIVIDENDS AND INVESTOR NETWORKS

Advances in Complex Systems ◽

10.1142/s0219525917500072 ◽

2017 ◽

Vol 20 (08) ◽

pp. 1750007 ◽

Cited By ~ 2

Author(s):

MATTHEW OLDHAM

Keyword(s):

Stock Market ◽

Complex Systems ◽

Financial Markets ◽

Network Topology ◽

Network Science ◽

Agent Based ◽

Excess Volatility ◽

Artificial Stock Market ◽

Analytical Frameworks ◽

Insight Into

The inability of investors and academics to consistently predict, and understand the behavior of financial markets has forced the search for alternative analytical frameworks. Analyzing financial markets as complex systems is a framework that has demonstrated great promises, with the use of agent-based models (ABMs) and the inclusion of network science playing an important role in increasing the relevance of the framework. Using an artificial stock market created via an ABM, this paper provides a significant insight into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. The paper demonstrates that the network topology that investors form and the dividend policy of firms significantly affect the behavior of the market. However, if investors have a bias to following their neighbors then the topology becomes redundant. By successfully addressing these issues this paper helps refine and shape a variety of additional research tasks for the use of ABMs in uncovering the dynamics of financial markets.

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Evolution of Agents in a Simple Artificial Market

Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies ◽

10.4018/978-1-60566-898-7.ch007 ◽

2010 ◽

pp. 118-133

Author(s):

Hiroshi Sato ◽

Masao Kubo ◽

Akira Namatame

Keyword(s):

Stock Market ◽

Comparative Study ◽

Wealth Distribution ◽

Trading Strategies ◽

The Other ◽

Complex Behavior ◽

Artificial Market ◽

Agent Based ◽

Other Hand ◽

Artificial Stock Market

In this chapter, we conduct a comparative study of various traders following different trading strategies. We design an agent-based artificial stock market consisting of two opposing types of traders: “rational traders” (or “fundamentalists”) and “imitators” (or “chartists”). Rational traders trade by trying to optimize their short-term income. On the other hand, imitators trade by copying the majority behavior of rational traders. We obtain the wealth distribution for different fractions of rational traders and imitators. When rational traders are in the minority, they can come to dominate imitators in terms of accumulated wealth. On the other hand, when rational traders are in the majority and imitators are in the minority, imitators can come to dominate rational traders in terms of accumulated wealth. We show that survival in a finance market is a kind of minority game in behavioral types, rational traders and imitators. The coexistence of rational traders and imitators in different combinations may explain the market’s complex behavior as well as the success or failure of various trading strategies. We also show that successful rational traders are clustered into two groups: In one group traders always buy and their wealth is accumulated in stocks; in the other group they always sell and their wealth is accumulated in cash. However, successful imitators buy and sell coherently and their wealth is accumulated only in cash.

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MICROSTRUCTURE DYNAMICS AND AGENT-BASED FINANCIAL MARKETS: CAN DINOSAURS RETURN?

Advances in Complex Systems ◽

10.1142/s0219525912500609 ◽

2012 ◽

Vol 15 (supp02) ◽

pp. 1250060 ◽

Cited By ~ 4

Author(s):

MICHAEL KAMPOURIDIS ◽

SHU-HENG CHEN ◽

EDWARD TSANG

Keyword(s):

Stock Market ◽

Genetic Programming ◽

Financial Markets ◽

Trading Strategies ◽

Self Organizing Maps ◽

Market Models ◽

Agent Based ◽

Time Periods ◽

Artificial Stock Market ◽

Rule Inference

This paper formalizes observations made under agent-based artificial stock market models into a concrete hypothesis, which is called the Dinosaur Hypothesis. This hypothesis states that the behavior of financial markets constantly changes and that the trading strategies in a market need to continuously co-evolve with it in order to remain effective. After formalizing the hypothesis, we suggest a testing methodology and run tests under 10 international financial markets. Our tests are based on a framework that we recently developed, which used Genetic Programming as a rule inference engine, and Self-Organizing Maps as a clustering machine for the above rules. However, an important assumption of that study was that maps among different periods were directly comparable with each other. In order to allow this to happen, we had to keep the same clusters throughout the different time periods of our experiments. Nevertheless, this assumption could be considered as strict or even unrealistic. In this paper, we relax this assumption. This makes our model more realistic. In addition, this allows us to investigate in depth the dynamics of market behavior and test for the plausibility of the Dinosaur Hypothesis. The results show that indeed markets' behavior constantly changes. As a consequence, strategies need to continuously co-evolve with the market; if they do not, they become obsolete or dinosaurs.

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Program trading and its risk analysis based on agent-based computational finance

International Journal of Financial Engineering ◽

10.1142/s2424786315500140 ◽

2015 ◽

Vol 02 (02) ◽

pp. 1550014 ◽

Cited By ~ 1

Author(s):

Xiong Xiong ◽

Hailiang Yuan ◽

Wei Zhang ◽

Yongjie Zhang

Keyword(s):

Stock Market ◽

Stock Index ◽

Stock Index Futures ◽

Experimental Conditions ◽

Price Expectation ◽

Agent Based ◽

Index Futures ◽

Artificial Stock Market ◽

Arbitrage Strategy

Program trading originates from combination trading technology in 70's in America. It was popular, but once it was considered as root of disaster. Nowadays, there are many divergences on program trading risk in international academic world. This essay is to analyze program trading on risk of stock market. The method adopts computational experiment to build artificial stock market under various experimental conditions. The research will consider two strategies: combination insurance strategy and arbitrage strategy to inspect stock index futures' influences on artificial stock market. Through contrast experiments, it finds that program trading will cause abnormal fluctuation of stock market in short-term period but it will have slight impact on fluctuation of stock market in long-term period. On the whole, stock index futures reduce price fluctuation of spot market. Besides, the research finds that combination insurance strategy will increase short selling expectation in pessimistic market to accelerate market collapse when the market gives the same downside price expectation and the market should consider the influence of combination insurance strategy.

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